Heatmap wip

app.py

@@ -1,4 +1,5 @@
 import json
+from urllib import request
 from fastapi import FastAPI
 from starlette.middleware.sessions import SessionMiddleware
 from starlette.responses import HTMLResponse, RedirectResponse
@@ -12,6 +13,8 @@ import pandas as pd
 import spotipy
 from spotipy import oauth2
 
+import heatmap
+
 PORT_NUMBER = 8080
 SPOTIPY_CLIENT_ID = 'c087fa97cebb4f67b6f08ba841ed8378'
 SPOTIPY_CLIENT_SECRET = 'ae27d6916d114ac4bb948bb6c58a72d9'
@@ -56,6 +59,15 @@ def scatter_plot_fn(request: gr.Request):
         value=iris,
     )
 
+def heatmap_plot_fn(request: gr.Request):
+    token = request.request.session.get('token')
+    if token:
+        sp = spotipy.Spotify(token)
+        data = heatmap.build_heatmap(heatmap.fetch_recent_songs(sp))
+        fig, _ = heatmap.plot(data)
+        return fig
+
+
 def get_features(spotify):
     features = []
     for index in range(0, 10):
@@ -95,7 +107,7 @@ with gr.Blocks() as demo:
         with gr.Column():
             with gr.Row():
                 with gr.Column():
-                    plot = gr.ScatterPlot(show_label=False).style(container=True)
+                    hm_plot = gr.Plot().style(container=True)
                 with gr.Column():
                     plot = gr.ScatterPlot(show_label=False).style(container=True)
             with gr.Row():
@@ -113,7 +125,7 @@ with gr.Blocks() as demo:
             value=[["something", "something", "something"], ["something", "something", "something"]]  # TODO: replace with actual reccomendations once get_started() is implemeted.
         )
     demo.load(fn=scatter_plot_fn, outputs=plot)
-
+    demo.load(fn=heatmap_plot_fn, output=hm_plot)
 
 
 gradio_app = gr.mount_gradio_app(app, demo, "/gradio")

heatmap.py

@@ -0,0 +1,40 @@
+from datetime import datetime
+from typing import List
+import numpy as np
+from spotipy import Spotify
+from dateutil.parser import parse
+import matplotlib.pyplot as plt
+
+def fetch_recent_songs(client: Spotify):
+	cursor = client.current_user_recently_played()
+	recently_played: List[dict] = cursor["items"]
+
+	max_iterations = 30
+	it = 0
+	while it < max_iterations and cursor["cursors"] is not None:
+		cursor = cursor["cursors"]["before"]
+		recently_played.extend(cursor["items"])
+
+	return recently_played
+
+def build_heatmap(recent_songs: List[dict]) -> np.ndarray:
+	heatmap = np.zeros((7, 20))
+	now = datetime.now()
+
+	for track in recent_songs:
+		played_at = parse(track["played_at"])
+		weekday = datetime.weekday(played_at)
+		week_offset = (now - played_at).days // 7
+		print(weekday, week_offset)
+
+		heatmap[weekday, -week_offset]
+	
+	return heatmap
+
+
+def plot(heatmap: np.ndarray):
+	fig, ax = plt.subplots()
+
+	ax.imshow(heatmap, cmap="Greens")
+	ax.set_yticklabels(["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"])
+	return fig, ax

requirements.txt

@@ -3,3 +3,4 @@ vega-datasets==0.9.0
 Authlib==1.2.0
 flask==2.2.2
 pandas==1.5.2
+matplotlib